Bose, Arup,
Patterned Random Matrices / Arup Bose (Indian Statistical Institute, Kolkata, West Bengal, India). - First edition. - 1 online resource (xxi, 267 pages)
chapter 1 A unified framework / chapter 2 Common symmetric patterned matrices / chapter 3 Patterned matrices / chapter 4 k-Circulant matrices / chapter 5 Wigner-type matrices / chapter 6 Balanced Toeplitz and Hankel matrices / chapter 7 Patterned band matrices / chapter 8 Triangular matrices / chapter 9 Joint convergence of i.i.d. patterned matrices / chapter 10 Joint convergence of independent patterned matrices / chapter 11 Autocovariance matrix / Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose.
"Large dimensional random matrices (LDRM) with specific patterns arise in econometrics, computer science, mathematics, physics, and statistics. This book provides an easy initiation to LDRM. Through a unified approach, we investigate the existence and properties of the limiting spectral distribution (LSD) of different patterned random matrices as the dimension grows. The main ingredients are the method of moments and normal approximation with rudimentary combinatorics for support. Some elementary results from matrix theory are also used. By stretching the moment arguments, we also have a brush with the intriguing but difficult concepts of joint convergence of sequences of random matrices and its ramifications. This book covers the Wigner matrix, the sample covariance matrix, the Toeplitz matrix, the Hankel matrix, the sample autocovariance matrix and the k-Circulant matrices. Quick and simple proofs of their LSDs are provided and it is shown how the semi-circle law and the March enko-Pastur law arise as the LSDs of the first two matrices. Extending the basic approach, we also establish interesting limits for some triangular matrices, band matrices, balanced matrices, and the sample autocovariance matrix. We also study the joint convergence of several patterned matrices, and show that independent Wigner matrices converge jointly and are asymptotically free of other patterned matrices.Arup Bose is a Professor at the Indian Statistical Institute, Kolkata, India. He is a distinguished researcher in Mathematical Statistics and has been working in high-dimensional random matrices for the last fifteen years. He has been the Editor of Sankyh for several years and has been on the editorial board of several other journals. He is a Fellow of the Institute of Mathematical Statistics, USA and all three national science academies of India, as well as the recipient of the S.S. Bhatnagar Award and the C.R. Rao Award. His forthcoming books are the monograph, Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee), to be published by Chapman & Hall/CRC Press, and a graduate text, U-statistics, M-estimates and Resampling (with Snigdhansu Chatterjee), to be published by Hindustan Book Agency."--Provided by publisher.
9780429948893 (e-book: PDF)
10.1201/9780429488436 doi
Random matrices.
QA196.5 / .B67 2018
512.9434 / B743
Patterned Random Matrices / Arup Bose (Indian Statistical Institute, Kolkata, West Bengal, India). - First edition. - 1 online resource (xxi, 267 pages)
chapter 1 A unified framework / chapter 2 Common symmetric patterned matrices / chapter 3 Patterned matrices / chapter 4 k-Circulant matrices / chapter 5 Wigner-type matrices / chapter 6 Balanced Toeplitz and Hankel matrices / chapter 7 Patterned band matrices / chapter 8 Triangular matrices / chapter 9 Joint convergence of i.i.d. patterned matrices / chapter 10 Joint convergence of independent patterned matrices / chapter 11 Autocovariance matrix / Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose -- Arup Bose.
"Large dimensional random matrices (LDRM) with specific patterns arise in econometrics, computer science, mathematics, physics, and statistics. This book provides an easy initiation to LDRM. Through a unified approach, we investigate the existence and properties of the limiting spectral distribution (LSD) of different patterned random matrices as the dimension grows. The main ingredients are the method of moments and normal approximation with rudimentary combinatorics for support. Some elementary results from matrix theory are also used. By stretching the moment arguments, we also have a brush with the intriguing but difficult concepts of joint convergence of sequences of random matrices and its ramifications. This book covers the Wigner matrix, the sample covariance matrix, the Toeplitz matrix, the Hankel matrix, the sample autocovariance matrix and the k-Circulant matrices. Quick and simple proofs of their LSDs are provided and it is shown how the semi-circle law and the March enko-Pastur law arise as the LSDs of the first two matrices. Extending the basic approach, we also establish interesting limits for some triangular matrices, band matrices, balanced matrices, and the sample autocovariance matrix. We also study the joint convergence of several patterned matrices, and show that independent Wigner matrices converge jointly and are asymptotically free of other patterned matrices.Arup Bose is a Professor at the Indian Statistical Institute, Kolkata, India. He is a distinguished researcher in Mathematical Statistics and has been working in high-dimensional random matrices for the last fifteen years. He has been the Editor of Sankyh for several years and has been on the editorial board of several other journals. He is a Fellow of the Institute of Mathematical Statistics, USA and all three national science academies of India, as well as the recipient of the S.S. Bhatnagar Award and the C.R. Rao Award. His forthcoming books are the monograph, Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee), to be published by Chapman & Hall/CRC Press, and a graduate text, U-statistics, M-estimates and Resampling (with Snigdhansu Chatterjee), to be published by Hindustan Book Agency."--Provided by publisher.
9780429948893 (e-book: PDF)
10.1201/9780429488436 doi
Random matrices.
QA196.5 / .B67 2018
512.9434 / B743